Positron-emission tomography for hepatocellular carcinoma: Current status and future prospects

Background: Kinetic parameters estimated with dynamic ¹⁸F-FDG PET/CT can help to characterize hepatocellular carcinoma (HCC). We aim to evaluate the feasibility of the gravitational search algorithm (GSA) for kinetic parameter estimation and to propose a dynamic chaotic gravitational search algorithm (DCGSA) to enhance parameter estimation. Methods: Five-minute dynamic PET/CT data of 20 HCCs were prospectively enrolled, and the kinetic parameters k₁ ~ k₄ and the hepatic arterial perfusion index (HPI) were estimated with a dual-input three-compartment model based on nonlinear least squares (NLLS), GSA and DCGSA. Results: The results showed that there were significant differences between the HCCs and background liver tissues for k₁, k₄ and the HPI of NLLS; k₁, k₃, k₄ and the HPI of GSA; and k₁, k₂, k₃, k₄ and the HPI of DCGSA. DCGSA had a higher diagnostic performance for k₃ than NLLS and GSA. Conclusions: GSA enables accurate estimation of the kinetic parameters of dynamic PET/CT in the diagnosis of HCC, and DCGSA can enhance the diagnostic performance.

We aimed to differentiate tuberculous peritonitis (TBP) from peritoneal carcinomatosis (PC) using a visual positron-emission tomography/computed tomography (PET/CT) scoring system based on mesenteric fluorodeoxyglucose (FDG) uptake. PET/CT scans from 31 patients with TBP and 92 patients with PC were retrospectively reviewed. A visual PET/CT scoring system for mesenteric FDG uptake was used according to the following characteristics: FDG uptake intensity (low = 0, moderate = 1, high = 2), FDG uptake deposits (uniform = 0, irregular = 1, ascitic = 2), FDG uptake focality (diffuse = 0, segmental = 1, focal = 2), nodularity on the corresponding CT (nonnodular = 0, micronodular = 1, macronodular = 2) and mesenteric lymphadenopathy (absent = 0, lymphadenopathy without FDG uptake = 1, lymphadenopathy with FDG uptake = 2). The FDG uptake intensity, deposits, focality, nodularity and mesenteric lymphadenopathy scores between TBP and PC were compared using chi-square tests. The diagnostic performance of this scoring system for differentiating TBP from PC was analyzed using a receiver operating characteristic (ROC) curve. P 1 had a sensitivity (the accuracy for diagnosis of PC) of 80.4% and a specificity (the accuracy for diagnosis of TBP) of 75.0%. A visual PET/CT scoring system based on mesenteric FDG uptake performed well in differentiating between TBP and PC.

Background: The endoplasmic reticulum plays an important role in glucose metabolism and has not been explored in the kinetic estimation of hepatocellular carcinoma (HCC) via 18F-fluoro-2-deoxy-d-glucose PET/CT. Methods: A dual-input four-compartment (4C) model, regarding endoplasmic reticulum was preliminarily used for kinetic estimation to differentiate 28 tumours from background liver tissue from 24 patients with HCC. Moreover, parameter images of the 4C model were generated from one patient with negative findings on conventional metabolic PET/CT. Results: Compared to the dual-input three-compartment (3C) model, the 4C model has better fitting quality, a close transport rate constant (K₁) and a dephosphorylation rate constant (k₆/k₄), and a different removal rate constant (k₂) and phosphorylation rate constant (k₃) in HCC and background liver tissue. The K₁, k₂, k₃, and hepatic arterial perfusion index (HPI) from the 4C model and k₃, HPI, and volume fraction of blood (V_b) from the 3C model were significantly different between HCC and background liver tissues (all P < 0.05). Meanwhile, the 4C model yielded additional kinetic parameters for differentiating HCC. The diagnostic performance of the top ten genes from the most to least common was HPI(4C), V_b(3C), HPI(3C), SUVmax, k₅(4C), k₃(3C), k₂(4C), v(4C), K₁(4C) and V_b(4C). Moreover, a patient who showed negative findings on conventional metabolic PET/CT had positive parameter images in the 4C model. Conclusions: The 4C model with the endoplasmic reticulum performed better than the 3C model and produced additional useful parameters in kinetic estimation for differentiating HCC from background liver tissue.

Background: Kinetic estimation provides fitted parameters related to blood flow perfusion and fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) transport and intracellular metabolism to characterize hepatocellular carcinoma (HCC) but usually requires 60 min or more for dynamic PET, which is time-consuming and impractical in a busy clinical setting and has poor patient tolerance. Methods: This study preliminarily evaluated the equivalence of liver kinetic estimation between short-term (5-min dynamic data supplemented with 1-min static data at 60 min postinjection) and fully 60-min dynamic protocols and whether short-term ¹⁸F-FDG PET-derived kinetic parameters using a three-compartment model can be used to discriminate HCC from the background liver tissue. Then, we proposed a combined model, a combination of the maximum-slope method and a three-compartment model, to improve kinetic estimation. Results: There is a strong correlation between the kinetic parameters K₁ ~ k₃, HPI and $${{\varvec{V}}}_{{\varvec{b}}}$$V b in the short-term and fully dynamic protocols. With the three-compartment model, HCCs were found to have higher k₂, HPI and k₃ values than background liver tissues, while K₁, k₄ and $${{\varvec{V}}}_{{\varvec{b}}}$$V b values were not significantly different between HCCs and background liver tissues. With the combined model, HCCs were found to have higher HPI, K₁ and k₂, k₃ and $${{\varvec{V}}}_{{\varvec{b}}}$$V b values than background liver tissues; however, the k₄ value was not significantly different between HCCs and the background liver tissues. Conclusions: Short-term PET is closely equivalent to fully dynamic PET for liver kinetic estimation. Short-term PET-derived kinetic parameters can be used to distinguish HCC from background liver tissue, and the combined model improves the kinetic estimation. Clinical relevance statement: Short-term PET could be used for hepatic kinetic parameter estimation. The combined model could improve the estimation of liver kinetic parameters. Graphical